Speeding up systems biology simulations of biochemical pathways using condor

Abstract

Systems biology is a scientific field that uses computational modelling to study biological and biochemical systems. The simulation and analysis of models of these systems typically explore behaviour over a wide range of parameter values; as such, they are usually characterised by the need for non-trivial amounts of computing power. Grid computing provides access to such computational resources. In previous research we created the grid-enabled Biochemical Networks Simulation Environment (BioNessieG) to attempt to speedup system biology simulations over a grid (the UK National Grid Service and ScotGrid). Following on from this work, we have created the SIMAP Utility, a standalone simulation tool dedicated to the modelling and analysis of the EGFR-MAP kinase pathway. This builds on experiences from BioNessieG by decoupling the simulation modelling elements from the Grid middleware. This new utility enables us to interface with different grid technologies. This paper therefore describes the new SIMAP Utility and an empirical investigation of its performance when deployed over a desktop grid based on the High Throughput Computing middleware Condor. We present our results based on a case study with a model of the mammalian ErbB signaling pathway, a pathway strongly linked to cancer.

Nuevos Aportes de las Tecnologías de Información para el Desarrollo de Simulación Distribuida

“…En los últimos diez años, las aplicaciones que brindan soporte a este tipo de simulación distribuida se han centrado en dos alternativas: i) uso de recursos de procesamiento fijos (como, por ejemplo, una red de computadoras), y ii) uso de recursos de computación en la nube bajo demanda. En el primer caso, se destacan el sistema WINGRID diseñado para acelerar las simulaciones de riesgo crediticio en bancos europeos (Mustafee & Taylor, 2009) y el uso de redes de computadoras para la simulación de las vías bioquímicas en el cáncer (Liu et al, 2014). Por su parte, en el segundo caso se destacan dos plataformas de desarrollo: JADES (Rak, Cuomo & Villano, 2012) y CloudSME (Taylor et al, 2014).…”

Distributed simulation: state-of-the-art and potential for operational research

“…For example, developed the WINGRID desktop grid system that was used to speed up credit risk simulations in a well-known European bank. Experiences from this formed the basis for SAKERGRID (Kite, et al, 2011), a desktop grid and computing cluster system in use at Saker Solutions and Sellafield PLC, a cluster-based high performance simulation system in use in the Ford Motor Company and a desktop grid that was used for simulations of biochemical pathways in cancer (Liu et al, 2014). Choi, Seo, and Kim (2014) also developed a similar system for use with dedicated computing clusters.…”

High Speed Simulation Analytics

“…More recent ones essentially use the same techniques but instead of fixed computing resources these use virtualised ones made available on a cloud. Examples of both of these include: the WINGRID desktop grid system that was used to speed up credit risk simulations in a well-known European bank (Mustafee & Taylor, 2009), SakerGrid, a desktop grid and computing cluster system in use today at Saker Solutions and Sellafield PLC (Kite, et al, 2011), a cluster-based high performance simulation system in use in the Ford Motor Company, a desktop grid that was used for simulations of biochemical pathways in cancer (Liu et al, 2014), and a cluster computing based grid used for a similar application (Choi, Seo, and Kim (2014)). Examples of cloudbased systems include an adaptation of the JADES platform to run agent-based simulations in parallel on cloud resources (Rak, Cuomo, & Villano, 2012) and the CloudSME Simulation Platform is used to run simulation experiments over multiple clouds (S.J.E.…”